Physicochemical Alteration and Water Quality Index of Ede-Onyima Lake, Okarki-Engenni, in Rivers State, Nigeria
Bibliographic record
Abstract
Freshwater quality is deteriorating as a result of ongoing threats from both anthropogenic and natural sources, resulting in an overall loss of ecological integrity. To provide an easily-understandable summary of complex water quality data, water quality indices (WQIs) -the Canadian water quality index (CWQI 1.0) model -was used for two distinct purposes, to assess the portability of the water and its suitability for the protection of aquatic life. The Canadian Council of Ministers Environment's water quality index (CCME WQI) was calculated by combining three variables: scope (F1), frequency (F2), and amplitude (F3), to produce a single value between 0 (worst) and 100 (best) representing the water quality. Predominantly impacted by the F3, which resulted in a WQI score of 32. Ede Onyima lake was ranked “poor” indicating that it is unfit for human consumption and aquatic life protection. The lake was impaired by high turbidity (86 NTU), trace metals such as Fe (20.73 mg/L), Mg (8.67 mg/L), and Mn (5.92 mg/L) loads. Their remobilization during turbulent flow portends a harmful effect on the Ede Onyima's water quality, indicating the critical need for a cohesive lake watershed management system to sustain conservation purposes of the lake and sustenance lake-dependent livelihoods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".